The Validity of Neurodynamic Tests in the Lower Limb Part 2 Straight Leg Raise and Slump Test

July 17, 2017 by physicalrevolution No Comments »

“The impact of neurodynamic testing on the perception of experimentally induced muscle pain” Coppetiers et al 2005

Again, similar to the LAI et al 2012 paper, this paper sought to look at the Validity of the Straight Leg Raise and Slump test. Similarly it could really only look at the SPECIFICITY of the tests. So they injected saline into the Tibialis Anterior muscle on 15 people and performed the SLR on them. They injected saline into the soleus muscle in 10 people and performed a neurodynamic slump. Again like the LAI 2012 study they took the SLR and Slump to just short of stretch sensation (Again was this enough to implicate the nerve ?)

For the slump test, they added each component in turn and performed it 3 times on each participant – 1st time prior to the injection and then 2 post injection tests. There was a tendenccy for pain to decrease with time – this was either due to dispersion of the saline with movement or due to stimulation of joint and ligament afferents which cause spinal and supraspinal effects on pain. They nicely justified the range to which they took patients into the neurodynamic test by stating that this equalled the range of movement at which point symptoms were elicited in a group of symptomatic patients with LBP +/- ipsilateral leg pain.

In this study they did not find a significant increase in pain score in either Slump or SLR which like the LAI study may add some weight to the SPECIFICITY of these two neurodynamic tests.


The Validity of Neurodynamic Tests in the Lower Limb Part 1 Femoral Slump

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From my page on Specificity and Sensitivity we can draw upon a meaning of Validity of a test and use the following paper as a real life example of the explanations on that page. So I perform in my clinic let’s say the femoral slump test to work out whether the pain that my patient is experiencing in their anterior thigh or knee for example is emanating from Nerve, Muscle or Joint. Neurodynamic tests are designed to implicate the nerve as a source (Not the sole source) of the patient’s pain. They let us know whether there is some mechanosensitivity in the nerve.

Reading this paper: “Specificity of the femoral slump for assessment of experimentally induced Anterior Knee Pain” by Lai et al 2012 offers some information as to whether or not the test is itself capable of differentiating between pain stemming from the muscle as source or the nerve. They took 12 asymptomatic people and injected saline into their fat pads in the knee. The people then rated the intensity of their pain before performing the femoral slump test by nodding the neck into flexion. They placed the patient just short of the sensation of stretch because the sensation of stretch may get confused with the sensation of pain (See article on “Stretching”). They were then placed into one of 3 groups – 1. Group whose pain decreased with the test 2. Group whose pain increased with the test 3. No change group. They found that the neck nodding did not significantly alter the the anterior knee pain.

So what is this test looking at in terms of validity. Well Validity is a combination of sensitivity and specificity. This test is looking at the number of TRUE NEGATIVES since we know that their pain is not stemming from the nerve because the saline was NOT injected into a NERVE. So we would therefore presume that the femoral slump test which seeks to implicate the nerve as a source, would test negative if it were a valid test of nerve as a source. So this paper is looking at the SPECIFICITY of the Femoral slump test. In this case it was found to be quite SPECIFIC because 75% of the people tested ( ie 9 people) did NOT report a change in their pain. BUT 3 did. So the SPECIFICITY is 75% as determined by this paper. SO what can we take away from this paper ?

Well it does not mean that EVERY patient on whom I perform this test who tests POSITIVE (Remember SPPIN Mnemonic) is guaranteed to have nerve sensitivity as the source of their pain. This is because the test was 75% SPECIFIC – so anyone quoting this paper and telling me that because the test is 75% specific it means their patient has nerve as a source of symptoms is not correct. We have to take other factors into consideration (absence or presence of Paraesthesia etc). All this paper tells us is that 12 people with saline injected into their fat pad on the most part tested negative for nerve mechanosensitivity in a test thought to implicate the femoral nerve more as a source of symptoms. The questions I would have to ask myself are: does the injection of saline into the fat pad replicate the same pain mechanism as the patient who comes into my clinic with pain in the front of their knee ? Also this was only performed on 12 individuals.

Also to really test the validity of the Femoral slump test – I would also want to test it’s SENSITIVITY – i.e it’s ability to identify TRUE POSITIVES – that would involve injecting the femoral nerve with saline so that we know that the femoral nerve has been sensitised and then carrying out the test to see how many POSITIVES i got. The problem with this is that it is a little more unethical and unpleasant to spike a nerve with saline !

Also just because my neurodynamic test is negative DOES not rule out the nerve as source because we may not be able to winde the nerve up sufficiently enough in this test to be able to rule it out as a source. BUT this is where you would have to correlate the extent to which you are winding the nerve up to elicit symptoms and the SIN factor of the patient’s presentation perhaps.


Sensitivity and Specificity

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I always get confused as to the meaning of Specificity and Sensitivity when it comes to interpreting the validity (validity is the ability of a “test” to actually measure what we think it is measuring – for example if we apply a test designed to quantify the number of people in a room who have a rotator cuff tear – does the test do what we presume it can do – ie identify the number of people with a rotator cuff tear in the room) of the numerous physical tests that therapists use in arriving at a diagnosis for patients. I was always quite skeptical of the percentage figures that papers arrive at when evaluating the value of a certain diagnostic test. Equally, I often felt uneasy when Therapists would reel off the fact that the tests they performed were 80 % specific and/or sensitive and used this to confidently put forward a clinical diagnosis. This is mainly because these figures have been calculated on a very narrow patient range which may or may not represent the person that sits in front of the therapist. To this end the confidence interval is important – this tells us the range so that given the same population and on a different occasion the figure could be as low as the lower end of the confidence interval.

However, here is what I have managed to understand from reading around a bit.  Sensitivity is the proportion of people with the “condition of interest” (on whom we are applying a certain test) who will have a positive result.  A test that correctly identifies a person who actually has the condition is termed a TRUE POSITIVE. Very highly sensitive tests are able to determine alot of the people who actually have the condition as positive (seeks to identify TRUE POSITIVES), however what it might do also is identify alot of people who do not have the condition as positive – this is termed a FALSE POSITIVE. So lets say 100 people come into the clinic, 50 of whom have a tear in their rotator cuff muscle (a muscle in the shoulder) and 50 do not, a very highly sensitive test may indeed identify almost all of the 50 people with an actual cuff tear as POSITIVE (TRUE POSITIVES) but it may also identify alot of people as positive who do NOT have a tear (FALSE POSITIVES). So we could interpret the test in another way and say, if the test is negative and it is able to identify pretty much all of the people with tears then any negative test would tell us that the person does not have a tear. (This is because there are FEW FALSE NEGATIVES with a highly sensitive test). So a mnemonic was devised : SNNOUT (SeNsitive tests when Negative, rule the condition OUT).

Now then, how do we work out a sensitivity value ? ie a percentage value.  For example a 100% Sensitive test, run on a cohort of 100 people with the condition we are testing would identify all 100 people as TRUE POSITIVE. Another example:

Let’s say the size of the population is 100 and the prevalence of say a rotator cuff tear is 30 out of 100 ie 30% (this means that we know in a certain cohort of patients – let’s say those over the age of 40 years old – the prevalence of a cuff tear might be 30% – ie 30 out of the 100 have a rotator cuff tear (NOT STRICTLY TRUE but just an example), we apply a test and get the result below:



We can see that the test managed to identify 24 people as TRUE POSITIVE but it also identified 14 FALSE POSITIVES, 6 FALSE NEGATIVES and 56 TRUE NEGATIVES. We calculate the test’s SENSITIVITY by looking at how many of the TRUE POSITIVES it was able to identify ie 24/30 = 0.8. Multiply this by 100 to give us our percentage sensitivity value ie 80%. So this test is 80% sensitive. It is failing to identify 20% of the people who actually have the disease.

Antithetically, the SPECIFICITY of a test – is the test’s ability to identify people who DO NOT have the condition we are testing for i.e SPECIFICITY is the tests ability to identify TRUE NEGATIVES ie of the people who DO NOT have the condition, how many are correctly identified. So if 100 people did not have the condition who underwent a 100% SPECIFIC test – all should test negative. HOWEVER, a highly SPECIFIC test may be able to identify most of the people as negative who do NOT have the condition but in doing so it may well identify alot of people who do have the condition as negative also (FALSE NEGATIVES). So in contrast to a SENSITIVE test, a highly specific test would have few FALSE POSITIVES. Therefore a SPecific test when testing Positive would rule the condition IN (Hence the mnemonic SPPIN). So a test for a rotator cuff tear that is highly SPECIFIC which comes back negative does NOT mean that they have not got a rotator cuff tear because as stated above: a highly SPECIFIC test identifies the TRUE NEGATIVES but in doing so, it also captures a number of FALSE NEGATIVES. BUT if that test came back positive and the test was highly SPECIFIC – then we would be pretty confident that they HAVE got a rotator cuff tear.

SO in the example above, the test identified 56 TRUE NEGATIVES and missed 14 TRUE NEGATIVES – because these were identified as FALSE POSITIVE. So it identified 56/(56+14) = 56/70 = 80% so the SPECIFICITY of this test is 80%.

There is something called the POSITIVE PREDICTIVE VALUE – this tells us what the chance is that a POSITIVE result is actually POSITIVE. So to calculate this – we look at ALL of the positive results (ie the shaded area in the picture) – there were 24 TRUE POSITIVES and 14 FALSE POSITIVES – so it identified 24 out of a total of 38 (24+14) positives ie 63%. This tells us that 63% of the POSITIVE results are correct. It’s PPV is 63%.

The NEGATIVE PREDICTIVE VALUE looks at the negative results in the chart (the unshaded area in the picture). There were 56 TRUE NEGATIVES and 6 FALSE NEGATIVES ie it determined 56/62 ie 90% correctly. So the NEGATIVE PREDICTIVE VALUE is 90% – ie the chances of the NEGATIVE result actually being NEGATIVE is 90%.

This would make sense because we have stated from the outset that the prevalence of a Rotator cuff tear in this cohort of people was 30% – ie it was LOW – so it seems obvious then that the chance of the test coming back NEGATIVE would be higher than it coming back POSITIVE. So we can see that the PPV and the NPV changes if the prevalence (i.e the number of people WITH or WITHOUT the condition in the cohort you are testing, changes) i.e prevalence is the probability that a person has a condition before we do the test.

This means that we need to take into consideration the prevalence of that condition for which we are testing in the person in front of us. For example if i have a 20 year old patient in front of me with shoulder pain who tests positive for a rotator cuff tear which uses a test which is 80% specific and 80% sensitive – i need to remember that in actual fact the prevalence of a rotator cuff tear in such a young person is actually very small. This begs the question – “How was the Specificity and Sensitivity values arrived at in the first place ?” – what Research paper calculated this figure and on what kind of population ? if they calculated it on a bunch of people over the age of 65 (let’s say) then the values do not really reflect the 20 year old patient sat in front of me when I perform the test.

Another important aspect to remember is – when the values were calculated – how were they sure that the patient had a rotator cuff tear in the first place. We need to be sure that the patient had a rotator cuff tear when we are measuring whether the test we are performing in the research paper is actually capable of measuring whether they do or don’t have a cuff tear. There must be a GOLD STANDARD measurement against which we can measure our test. I.e if we cut open the patients shoulder and have a look inside and see a tear with our eyes – we are then pretty much sure that they DO HAVE a cuff tear and the GOLD STANDARD against which we are measuring is the “cutting the shoulder open to look inside”.

Here is an example: let’s say the prevalence of a cuff tear is 33 in 100,000 and a rotator cuff tear test has a 94% Sensitivity and a 97% Specificity. So how many TRUE POSITIVES (sensitivity) will it identify? 94/100 x 33 = 31 TRUE POSITIVES leaving 2 FALSE NEGATIVES.

The test has a 97% SPECIFICITY so how many TRUE NEGATIVES will it identify ? Well we know that there are 100,000 – 33 ie 99967 TRUE NEGATIVES. So 97/100 x 99967 = 96968 TRUE NEGATIVES will be identified, leaving 99967-96968 ie 2999 which are TRUE NEGATIVE which aren’t identified and are FALSE POSITIVE. The PPV is 31/31+2999 x 100 = 1% ie 1% of the total number of positives ARE actually positive because there are so many FALSE POSITIVES. The NPV is 96968 (TRUE NEGATIVES) identified out of a total of 96968 +2 TRUE NEGATIVES (because there were 2 FALSE NEGATIVES) IE 96968/96970 X 100 = 99.99% NPV.

So despite the SPECIFICTY being so high ie 97% and we know that SPPIN (Specificity tests when positive rule the condition IN), in actual fact the probability that the positive result IS actually positive is only 1% !

HOWEVER we need to take other factors into consideration – let us say that the person in front of us was over 65 and had had a fall onto their arm recently and the arm was weak. Suddenly this person falls out of the REGULAR cohort of people on whom the Specificity and Sensitivity was calculated. The PPV does not now mean that much to us because the prevalence of a rotator cuff tear in a cohort of people over the age of 65 with a fall on the arm whose arm is weak may be as high as 90% – or put another way of 100,000 65 year olds who had fallen onto the arm and the arm was testing weak, 98,000 actually may have a cuff tear ie 98% prevalence. This dramatically changes the PPV figure.







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June 7, 2015 by physicalrevolution No Comments »

Bringing NHS care to the comfort of patients laptops and mobile phones…
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October 18, 2014 by physicalrevolution No Comments »

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August 7, 2014 by physicalrevolution No Comments »

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August 5, 2014 by physicalrevolution No Comments »

Spheres of movement and injury prevention – increase your sphere of movement and lower your injury risk…. read more…


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July 6, 2014 by physicalrevolution No Comments »

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July 2, 2014 by physicalrevolution No Comments »

In the first part of this series the importance of the shoulder blades we talked about getting re-acquainted with our shoulder blades and why they proved to be so alien to us. Well in this second part we are going to talk about how the position of your shoulder blade can potentially set you up for injury before you even begin to use your arm and how you can remedy it… read more…